|
--- |
|
language: |
|
- en |
|
pipeline_tag: tabular-classification |
|
tags: |
|
- Computational Neuroscience |
|
--- |
|
|
|
To reduce the effort in manual curation, we developed a machine learning approach using Neuropixels probes, |
|
incorporating quality metrics to identify noise clusters and isolate single-cell activity automatically. |
|
Compatible with the Spikeinterface API, our method generalizes across various probes and species. |
|
|
|
We generated a machine learning model trained on 11 mice in V1, SC, and ALM using Neuropixels on mice. |
|
Each recording was labeled by at least two people and in different combinations. |
|
The agreement amongst labelers is 80%. |
|
|
|
|
|
There are two tutorial notebooks: |
|
|
|
1. Model_based_curation_tutorial.ipynb |
|
|
|
This notebook helps you apply pretrained models to new recordings. Simply load the models and use them to label your spike-sorted data. |
|
|
|
|
|
We provide "noise_neuron_model.skops" which is used to identify noise, and "sua_mua_model.skops" which is used to isolate SUA. These models |
|
can be used if you want to predict labels (SUA,MUA and noise) on mice data generated using Neuropixels. |
|
|
|
Steps: |
|
|
|
1. load your recording depending on the acquisition software you used to create the 'recording' object |
|
2. load your sorting depending on the spike sorter you used to create the 'sorting' object |
|
3. Then you can create a Sorting_Analyzer object and you compute quality metrics. |
|
|
|
These steps are explained in more details in the jupyter notebook in files folder. |
|
|
|
auto_label_units is the main in this notebook. API link to know the parameters: |
|
(https://spikeinterface--2918.org.readthedocs.build/en/2918/api.html#spikeinterface.curation.auto_label_units) |
|
|
|
|
|
# example use of auto-label function |
|
from spikeinterface.curation import auto_label_units |
|
|
|
labels = auto_label_units( |
|
sorting_analyzer = sorting_analyzer, |
|
model_folder = “SpikeInterface/a_folder_for_a_model”, |
|
trusted = [‘numpy.dtype’]) |
|
|
|
|
|
|
|
|
|
2. Train_new_model.ipynb |
|
|
|
If you have your own manually curated data (e.g., from other species), this notebook allows you to train a new model using your specific data. |
|
Here you need to follow the three steps mentioned before but you need to provide your manually curated labels. |
|
|
|
train_model is the main function to do train your model. API Link: |
|
https://spikeinterface--2918.org.readthedocs.build/en/2918/api.html#spikeinterface.curation.train_model |
|
|
|
|
|
from spikeinterface.curation.train_manual_curation import train_model |
|
|
|
trainer = train_model(mode = "analyzers", |
|
labels = labels, |
|
analyzers = [labelled_analyzer, labelled_analyzer], |
|
output_folder = str(output_folder), |
|
imputation_strategies = None, |
|
scaling_techniques = None, |
|
classifiers = None) # Default to Random Forest only. Other classifiers you can try [ "AdaBoostClassifier", "GradientBoostingClassifier", |
|
# "LogisticRegression", "MLPClassifier", "XGBoost", "LightGBM", "CatBoost"] |
|
|
|
|
|
|
|
Acknowledgments: |
|
|
|
I would like to thank people who have helped a lot in this project: |
|
|
|
* For code refactoring and helping integration in Spikeinterface: Chris Halcrow, Jake Swann, Robyn Greene |
|
* Curators: Nilufar Lahiji, Sacha Abou Rachid, Severin Graff, Luca Koenig, Natalia Babushkina, Simon Musall |
|
* Advisors: Alessio Buccino, Matthias Hennig and Simon Musall |
|
|
|
Also all my amazing lab members : https://brainstatelab.wordpress.com/ |
